One of the first writer recognition methods using deep learning techniques was proposed by Fiel and Sablatnig [1]}. They trained the "caffenet" CNN on line and word segmentations. Feature vectors extracted from the penultimate layer of the CNN are compared using the \(\chi ^{2}\) -distance. At the time, their results showed superior performance on the IAM [2]} and ICFHR’12 datasets while being inferior on the ICDAR13 [3]} dataset. Christlein et al [4]} used activation features of from a CNN as image local descriptors. Afterwards, global images descriptors are formed by means of GMM supervector encoding. Their approach improved 0.21 % in terms of mAP on the ICDAR13 dataset. In another recent method by Christlein et al [5]} LeNet and ResNet architectures are employed to extract local descriptors followed by VLAD encoding to compute global image descriptors for document images. They experimented with both exemplar support vector machines (ESVMs) and nearest neighbours to evaluate their pipeline. To the best of our knowledge, their approach has set new standards on the ICDAR13 and CVL [6]} datasets.
